docs: def NS(s, o):
      """
          Nash Sutcliffe efficiency coefficient
          input:
          s: simulated
          o: observed
          output:
          ns: Nash Sutcliffe efficient coefficient
          """
      # s,o = filter_nan(s,o)
      return 1 - np.sum((s-o)**2)/np.sum((o-np.mean(o))**2)